Evidence map›Paper›PMID 41673983›Full record

SynthesisBritish journal of clinical pharmacology2026

Accelerating tacrolimus model-informed precision dosing in kidney transplant recipients: Model evaluation and refinement strategies.

Martín Umpiérrez, Cecilia Maldonado, Natalia Guevara, Magdalena Cardenas-Rodriguez, Clara Menéndez, Mariana Seija, José Santiago, Oscar Noboa, Manuel Ibarra

Abstract readSystematic Review
In one paragraph

Synthesis in British journal of clinical pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Martín UmpiérrezDepartment of Pharmaceutical Sciences, Faculty of Chemistry, Universidad de la República, Montevideo, Uruguay.ORCID https://orcid.org/0000-0002-1457-0894
Cecilia MaldonadoDepartment of Pharmaceutical Sciences, Faculty of Chemistry, Universidad de la República, Montevideo, Uruguay.ORCID https://orcid.org/0000-0002-9452-2111
Natalia GuevaraDepartment of Pharmaceutical Sciences, Faculty of Chemistry, Universidad de la República, Montevideo, Uruguay.ORCID https://orcid.org/0000-0002-5541-6316
Magdalena Cardenas-RodriguezMolecular and Human Genetics Laboratory, Institut Pasteur de Montevideo, Montevideo, Uruguay.ORCID https://orcid.org/0000-0001-9651-1830
Clara MenéndezMolecular and Human Genetics Laboratory, Institut Pasteur de Montevideo, Montevideo, Uruguay.
Mariana SeijaNephrology Center, Hospital de Clinicas Dr Manuel Quintela Faculty of Medicine, Universidad de la República, Montevideo, Uruguay.ORCID https://orcid.org/0000-0002-9773-104X
José SantiagoNephrology Center, Hospital de Clinicas Dr Manuel Quintela Faculty of Medicine, Universidad de la República, Montevideo, Uruguay.
Oscar NoboaNephrology Center, Hospital de Clinicas Dr Manuel Quintela Faculty of Medicine, Universidad de la República, Montevideo, Uruguay.ORCID https://orcid.org/0000-0002-0975-9924
Manuel IbarraDepartment of Pharmaceutical Sciences, Faculty of Chemistry, Universidad de la República, Montevideo, Uruguay.ORCID https://orcid.org/0000-0002-0484-6367

Funding

Comisión Sectorial de Investigación Científica 2113 CSIC I+D 2020
6 · The paper itself

Abstract

aimTo guide the efficient implementation of Model-Informed Precision Dosing (MIPD) by comparing the predictive performance of published tacrolimus population pharmacokinetic (popPK) models with an in-house model in Uruguayan kidney transplant recipients, using comprehensive external evaluation methodology.

methodsTwenty-five adult tacrolimus popPK models were identified through systematic review and externally evaluated using retrospective data from 39 patients (396 steady-state concentrations). In parallel, an in-house model was developed with prospective and retrospective data from 49 patients (727 concentrations). Predictive performance was assessed across different data integration levels: a priori predictions and sequential Bayesian forecasting with progressive incorporation of individual observations.

resultsAll published models and the in-house model showed poor a priori predictive performance, with even the best performing models yielding MAPE values near 50% and fewer than 30% of predictions within 30% of observed concentrations. Bayesian forecasting progressively improved bias, accuracy and dispersion of prediction errors. Initial incorporation of three trough concentrations produced limited benefit, but substantial improvements were observed upon integration of a pharmacokinetic curve composed of at least 3 post-dose concentrations, at which point several published models and the in-house model achieved high precision, with mean bias below 10% and MAPE below 30%. Two published models exhibited comparable performance to the in-house model, which achieved marginally better results as Bayesian forecasting incorporated successive individual observations, without providing a meaningful overall advantage.

conclusionPublished tacrolimus popPK models can achieve clinically reliable predictions when combined with Bayesian updating, performing comparably to in-house developed models. These findings support a resource-efficient strategy for implementing tacrolimus MIPD without the need for local model development, provided that a pharmacokinetic curve is obtained early post-transplantation.

Indexed as

Immunosuppressive AgentsKidney TransplantationModels, BiologicalTacrolimusAdultBayes TheoremDose-Response Relationship, DrugFemaleHumansMaleMiddle AgedPrecision MedicineProspective StudiesRetrospective StudiesImmunosuppressive AgentsTacrolimusBayesian forecastingexternal evaluationmodel‐informed precision dosingtacrolimus in kidney recipients

Identifiers

PMID41673983
PMCPMC13370053

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.